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arXiv · 2206.05700

A Functional Information Perspective on Model Interpretation

Itai Gat·Nitay Calderon·Roi Reichart·Tamir Hazan
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Abstract

Contemporary predictive models are hard to interpret as their deep nets exploit numerous complex relations between input elements. This work suggests a theoretical framework for model interpretability by measuring the contribution of relevant features to the functional entropy of the network with respect to the input. We rely on the log-Sobolev inequality that bounds the functional entropy by the functional Fisher information with respect to the covariance of the data. This provides a principled way to measure the amount of information contribution of a subset of features to the decision function. Through extensive experiments, we show that our method surpasses existing interpretability sampling-based methods on various data signals such as image, text, and audio.

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Itai Gat, Nitay Calderon, Roi Reichart, Tamir Hazan. 2022-06-12. A Functional Information Perspective on Model Interpretation. https://arxiv.org/abs/2206.05700

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